comparison src/Modules/BMM/ModuleGammatone_test.py @ 5:3c782dec2fc0

- Ported over HTK file output
- Added some more meat to the Slaney IIR gammatone implementation
- Ported over the AIM-MAT sf2003 parabola strobe algorithm
- Finished making the SAI implementation compile
- Ported over the strobe list class (now uses STL deques internally)

+#!/usr/bin/env python
+# encoding: utf-8
+#
+# AIM-C: A C++ implementation of the Auditory Image Model
+# http://www.acousticscale.org/AIMC
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+ModuleGammatone_test.py
+
+Created by Thomas Walters on 2010-02-15.
+Copyright 2010 Thomas Walters <[e-mail]>
+Test for the Slaney IIR gammatone.
+"""
+
+import aimc
+from scipy import io
+
+def main():
+  data_file = "src/Modules/BMM/testdata/gammatone.mat"
+  data = io.loadmat(data_file)
+  
+  # The margin of error allowed between the returned values from AIM-C and
+  # the stored MATLAB values.
+  epsilon = 0.000001;
+  
+  input_wave = data["input_wave"]
+  sample_rate = data["sample_rate"]
+  centre_frequencies = data["centre_frequencies"]
+  expected_output = data["expected_output"]
+  
+  (channel_count, buffer_length, frame_count) = expected_output.shape
+  
+  input_sig = aimc.SignalBank()
+  input_sig.Initialize(1, buffer_length, 44100)
+  parameters = aimc.Parameters()
+  mod_gt = aimc.ModuleGammatone(parameters)
+  mod_gt.Initialize(input_sig)
+  
+  correct_count = 0;
+  incorrect_count  = 0;
+  for p in range(0, profile_count):
+    profile = given_profiles[p]
+    features = matlab_features[p]
+    for i in range(0, channel_count):
+      profile_sig.set_sample(i, 0, profile[i])
+    mod_gauss.Process(profile_sig)
+    out_sig = mod_gauss.GetOutputBank()
+    error = False;
+    for j in range(0, out_sig.channel_count()):
+      if (abs(out_sig.sample(j, 0) - features[j]) > epsilon):
+        error = True;
+        incorrect_count += 1;
+      else:
+        correct_count += 1;
+    if error:
+      print("Mismatch at profile %d" % (p))
+      print("AIM-C values: %f %f %f %f" % (out_sig.sample(0, 0), out_sig.sample(1, 0), out_sig.sample(2, 0), out_sig.sample(3, 0)))
+      print("MATLAB values: %f %f %f %f" % (features[0], features[1], features[2], features[3]))
+      print("")
+  percent_correct = 100 * correct_count / (correct_count + incorrect_count)
+  print("Total correct: %f percent" % (percent_correct))
+  if percent_correct == 100:
+    print("=== TEST PASSED ===")
+  else:
+    print("=== TEST FAILED! ===")
+
+  pass
+
+
+if __name__ == '__main__':
+  main()